Final Presentation

Data Analyses of The Office

Today, I will be presenting to you (NBC Marketers) some data from The Office that will be used to consider future directions for an upcoming season. With this, we hope to inform show producers’ decisions and increase The Office’s potential viewership.

The Office

Background

The Office is a comedy mockumentary following the lifestyle of seemingly normal office workers at a branch of Dunder Mifflin Paper Company. It is an American show based off the original British version under the same name, adapted for NBC by Greg Daniels.

The Office Viewership

Considerations:

• Character Popularity

• Line Designation for Future Seasons

• Existing Trends

Who is the Favorite Character?

Ranker.com

A platform for user-generated rankings on various topics, including entertainment.

It seems Michael Scott has the public vote.

Method for Main Character Determination

As the show follows multiple characters, there is some debate as to who exactly the main character is.

• Method:

  • Count the amount of words spoken by each character per season

  • Understand who the show writers center most

Data

# Data 
library(tidyverse)

officelines <- read_csv("the-office_lines.csv")  
head(officelines)

# A tibble: 6 × 5
   ...1 Character Line                                     Season Episode_Number
  <dbl> <chr>     <chr>                                     <dbl>          <dbl>
1     0 Michael   All right Jim. Your quarterlies look ve…      1              1
2     1 Jim       Oh, I told you. I couldn’t close it. So…      1              1
3     2 Michael   So you’ve come to the master for guidan…      1              1
4     3 Jim       Actually, you called me in here, but ye…      1              1
5     4 Michael   All right. Well, let me show you how it…      1              1
6     5 Michael   [on the phone] Yes, I’d like to speak t…      1              1

Homogenizing Total Line Data

# Cleaning Data
officelines <- officelines |>
mutate(Line = str_to_lower(Line))

lines <- officelines$Line
lines <- str_remove_all(lines, "[[:punct:]]")

officelines <- officelines |>
select(Character, Line, Season, Episode_Number) |>
mutate(Line = lines) 

# Counting the words
words_per_char <- officelines |>
mutate(word_count = lengths(str_split(Line, "\\s+"))) |>
group_by(Character, Season) |>
summarize(total_words = sum(word_count))

• lowercase
• remove punctuation
• sum words said by character in a season

Most Relevant Characters

Selected via The Office Wiki

Cleaning Character List

distinct_char <- words_per_char |>
  distinct(Character)

distinct_char

# A tibble: 776 × 1
# Groups:   Character [776]
   Character           
   <chr>               
 1 (Pam’S Mom) Heleen  
 2 3Rd Athlead Employee
 3 4Th Athlead Employee
 4 A.J.                
 5 Aaron Rodgers       
 6 Abby                
 7 Abe                 
 8 Actor               
 9 Actress             
10 Ad Guy 1            
# ℹ 766 more rows

Characters like “Pam’s Mom” need to be removed! We should also should make sure to count things said by main characters together.

Filtering for Most Relevant Characters

#Keeping only Main Characters & Cleaning Data
main_characters <- c("Michael", "Jim", "Pam", "Dwight", "Andy", "Ryan", "Robert")

words_dont_keep <- c("Voicemail", "Mom", "Dad", "Ad", "Fake", "Except", "Church")

word_pattern_keep <- paste0("\\b(", paste(main_characters, collapse = "|"), ")\\b")

word_pattern_delete <- paste0("\\b(", paste(words_dont_keep, collapse = "|"), ")\\b")

get_rid_ands <- "\\s&\\s|\\sAnd\\s|/|,\\s"

words_per_char <- words_per_char |>
filter(str_detect(Character, word_pattern_keep)) |>
filter(!str_detect(Character, word_pattern_delete)) |>
mutate(Character = str_replace_all(Character, get_rid_ands, ", ")) |>
separate_rows(Character, sep = ", ") |>
filter(str_detect(Character, word_pattern_keep)) |>
mutate(Character = case_when(
str_detect(Character, "\\bJim\\b") ~ "Jim",
str_detect(Character, "\\bPam\\b") ~ "Pam",
str_detect(Character, "\\bDwight\\b") ~ "Dwight",
str_detect(Character, "\\bMichael\\b") ~ "Michael",
str_detect(Character, "\\bAndy\\b") ~ "Andy",
str_detect(Character, "\\bRobert\\b") ~ "Robert", 
str_detect(Character, "\\bRyan\\b") ~ "Ryan")) |>
group_by(Character, Season) |>
summarise(total_words = sum(total_words), .groups = 'drop') 

Final Dataset

words_per_char

# A tibble: 55 × 3
   Character Season total_words
   <chr>      <dbl>       <int>
 1 Andy           3        4867
 2 Andy           4        2924
 3 Andy           5        5624
 4 Andy           6        6776
 5 Andy           7        6611
 6 Andy           8       14452
 7 Andy           9        9157
 8 Dwight         1        3699
 9 Dwight         2       11895
10 Dwight         3       10226
# ℹ 45 more rows

Data Visualization

ggplot(words_per_char, aes(x = Character, y = total_words, fill = Character)) +
geom_bar(stat = "identity") +
facet_wrap(~Season) +
theme_minimal() +
labs(
title = "Who says the most words in The Office?",
subtitle = "Main characters selected via The Office Wiki",
x = "Season", 
y = "Total Words Spoken") +
  theme(axis.text.x = element_blank())

Takeaways

• Michael Scott’s Dominance in Word Count:

  • Michael consistently speaks the most words across all seasons where he appears.

• Drop in Michael’s Words After Season 7:

  • There is a sharp decline in Michael’s dialogue starting in Season 8 (after his departure).

  • Other characters, such as Andy, Dwight, and Jim, seem to fill the void, but none fully replace his prominence.

• Increased Focus on Other Characters in Later Seasons:

  • Andy’s role expands significantly in Seasons 8 and 9.

  • Jim and Dwight play a strong secondary role.

Main Character and Trend

To ensure the success of Season 10, producers should strongly consider bringing Michael Scott back as a central character. Michael Scott was not only a pivotal figure in the show’s narrative but also a driving force behind its immense popularity.

Reinstating Michael Scott as the main character would tap into the nostalgia of long-time fans while reigniting interest among lapsed viewers. By leveraging his role as the heart of the show, NBC can create a revitalized narrative that resonates with both loyal fans and new audiences, ensuring sustained and even increased viewership for the series.